Comparative study of CEC’2013 problem using dual population genetic algorithm
Автор: A. J. Umbarkar, L. R. Moon, P. D. Sheth
Журнал: International Journal of Information Engineering and Electronic Business @ijieeb
Статья в выпуске: 5 vol.10, 2018 года.
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Evolutionary Algorithms (EAs) are found to be effective for solving a large variety of optimization problems. In this Paper Dual Population Genetic Algorithm (DPGA) is used for solving the test functions of Congress on Evolutionary Computation 2013 (CEC’2013), by using two different crossovers. Dual Population Genetic Algorithm is found to be better in performance than traditional Genetic Algorithm. It is also able to solve the problem of premature convergence and diversity of the population in genetic algorithm. This paper proposes Dual Population Genetic Algorithm for solving the problem regarding unconstrained optimization. Dual Population Genetic Algorithm is used as meta-heuristic which is verified against 28 functions from Problem Definitions and Evaluation Criteria for the Congress on Evolutionary Computation 2013 on unconstrained set of benchmark functions using two different crossovers. The results of both the crossovers are compared with each other. The results of both the crossovers are also compared with the existing results of Standard Particle Swarm Optimization algorithm. The Experimental results showed that the algorithm found to be better for finding the solution of multimodal functions of the problem set.
Dual Population Genetic Algorithm, DPGA, Genetic Algorithm, GA, Evolutionary Algorithm, EA, Function Optimization, CEC’2013, k-Point Crossover
Короткий адрес: https://sciup.org/15016149
IDR: 15016149 | DOI: 10.5815/ijieeb.2018.05.06
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